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--dataset_name="huggan/flowers-102-categories" \ |
--output_dir="ddpm-ema-flowers-64" \ |
--mixed_precision="fp16" \ |
--push_to_hub |
</hfoption> |
<hfoption id="multi-GPU"> |
If you’re training with more than one GPU, add the --multi_gpu parameter to the training command: Copied accelerate launch --multi_gpu train_unconditional.py \ |
--dataset_name="huggan/flowers-102-categories" \ |
--output_dir="ddpm-ema-flowers-64" \ |
--mixed_precision="fp16" \ |
--push_to_hub |
</hfoption> |
</hfoptions> |
The training script creates and saves a checkpoint file in your repository. Now you can load and use your trained model for inference: Copied from diffusers import DiffusionPipeline |
import torch |
pipeline = DiffusionPipeline.from_pretrained("anton-l/ddpm-butterflies-128").to("cuda") |
image = pipeline().images[0] |
Schedulers 🤗 Diffusers provides many scheduler functions for the diffusion process. A scheduler takes a model’s output (the sample which the diffusion process is iterating on) and a timestep to return a denoised sample. The timestep is important because it dictates where in the diffusion process the step is; data is g... |
functionalities. ConfigMixin takes care of storing the configuration attributes (like num_train_timesteps) that are passed to |
the scheduler’s __init__ function, and the attributes can be accessed by scheduler.config.num_train_timesteps. Class attributes: _compatibles (List[str]) — A list of scheduler classes that are compatible with the parent scheduler |
class. Use from_config() to load a different compatible scheduler class (should be overridden |
by parent class). from_pretrained < source > ( pretrained_model_name_or_path: Union = None subfolder: Optional = None return_unused_kwargs = False **kwargs ) Parameters pretrained_model_name_or_path (str or os.PathLike, optional) — |
Can be either: |
A string, the model id (for example google/ddpm-celebahq-256) of a pretrained model hosted on |
the Hub. |
A path to a directory (for example ./my_model_directory) containing the scheduler |
configuration saved with save_pretrained(). |
subfolder (str, optional) — |
The subfolder location of a model file within a larger model repository on the Hub or locally. return_unused_kwargs (bool, optional, defaults to False) — |
Whether kwargs that are not consumed by the Python class should be returned or not. cache_dir (Union[str, os.PathLike], optional) — |
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache |
is not used. force_download (bool, optional, defaults to False) — |
Whether or not to force the (re-)download of the model weights and configuration files, overriding the |
cached versions if they exist. resume_download (bool, optional, defaults to False) — |
Whether or not to resume downloading the model weights and configuration files. If set to False, any |
incompletely downloaded files are deleted. proxies (Dict[str, str], optional) — |
A dictionary of proxy servers to use by protocol or endpoint, for example, {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request. output_loading_info(bool, optional, defaults to False) — |
Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. local_files_only(bool, optional, defaults to False) — |
Whether to only load local model weights and configuration files or not. If set to True, the model |
won’t be downloaded from the Hub. token (str or bool, optional) — |
The token to use as HTTP bearer authorization for remote files. If True, the token generated from |
diffusers-cli login (stored in ~/.huggingface) is used. revision (str, optional, defaults to "main") — |
The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier |
allowed by Git. Instantiate a scheduler from a pre-defined JSON configuration file in a local directory or Hub repository. To use private or gated models, log-in with |
huggingface-cli login. You can also activate the special |
“offline-mode” to use this method in a |
firewalled environment. save_pretrained < source > ( save_directory: Union push_to_hub: bool = False **kwargs ) Parameters save_directory (str or os.PathLike) — |
Directory where the configuration JSON file will be saved (will be created if it does not exist). push_to_hub (bool, optional, defaults to False) — |
Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the |
repository you want to push to with repo_id (will default to the name of save_directory in your |
namespace). kwargs (Dict[str, Any], optional) — |
Additional keyword arguments passed along to the push_to_hub() method. Save a scheduler configuration object to a directory so that it can be reloaded using the |
from_pretrained() class method. SchedulerOutput class diffusers.schedulers.scheduling_utils.SchedulerOutput < source > ( prev_sample: FloatTensor ) Parameters prev_sample (torch.FloatTensor of shape (batch_size, num_channels, height, width) for images) — |
Computed sample (x_{t-1}) of previous timestep. prev_sample should be used as next model input in the |
denoising loop. Base class for the output of a scheduler’s step function. KarrasDiffusionSchedulers KarrasDiffusionSchedulers are a broad generalization of schedulers in 🤗 Diffusers. The schedulers in this class are distinguished at a high level by their noise sampling strategy, the type of network and scaling, th... |
The name of the repository you want to push your model, scheduler, or pipeline files to. It should |
contain your organization name when pushing to an organization. repo_id can also be a path to a local |
directory. commit_message (str, optional) — |
Message to commit while pushing. Default to "Upload {object}". private (bool, optional) — |
Whether or not the repository created should be private. token (str, optional) — |
The token to use as HTTP bearer authorization for remote files. The token generated when running |
huggingface-cli login (stored in ~/.huggingface). create_pr (bool, optional, defaults to False) — |
Whether or not to create a PR with the uploaded files or directly commit. safe_serialization (bool, optional, defaults to True) — |
Whether or not to convert the model weights to the safetensors format. variant (str, optional) — |
If specified, weights are saved in the format pytorch_model.<variant>.bin. Upload model, scheduler, or pipeline files to the 🤗 Hugging Face Hub. Examples: Copied from diffusers import UNet2DConditionModel |
unet = UNet2DConditionModel.from_pretrained("stabilityai/stable-diffusion-2", subfolder="unet") |
# Push the `unet` to your namespace with the name "my-finetuned-unet". |
unet.push_to_hub("my-finetuned-unet") |
# Push the `unet` to an organization with the name "my-finetuned-unet". |
unet.push_to_hub("your-org/my-finetuned-unet") |
AutoPipeline 🤗 Diffusers is able to complete many different tasks, and you can often reuse the same pretrained weights for multiple tasks such as text-to-image, image-to-image, and inpainting. If you’re new to the library and diffusion models though, it may be difficult to know which pipeline to use for a task. For ex... |
import torch |
pipeline = AutoPipelineForText2Image.from_pretrained( |
"runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True |
).to("cuda") |
prompt = "peasant and dragon combat, wood cutting style, viking era, bevel with rune" |
image = pipeline(prompt, num_inference_steps=25).images[0] |
image Under the hood, AutoPipelineForText2Image: automatically detects a "stable-diffusion" class from the model_index.json file loads the corresponding text-to-image StableDiffusionPipeline based on the "stable-diffusion" class name Likewise, for image-to-image, AutoPipelineForImage2Image detects a "stable-diffusion"... |
import torch |
import requests |
from PIL import Image |
from io import BytesIO |
pipeline = AutoPipelineForImage2Image.from_pretrained( |
"runwayml/stable-diffusion-v1-5", |
torch_dtype=torch.float16, |
use_safetensors=True, |
).to("cuda") |
prompt = "a portrait of a dog wearing a pearl earring" |
url = "https://upload.wikimedia.org/wikipedia/commons/thumb/0/0f/1665_Girl_with_a_Pearl_Earring.jpg/800px-1665_Girl_with_a_Pearl_Earring.jpg" |
response = requests.get(url) |
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